Machine learning methods and systems for characterizing corn growth efficiency

ABSTRACT

A computing system includes a processor; and one or more non-transitory, computer-readable storage media storing: a trained machine learning model; and machine-readable instructions that, when executed by the one or more processors, cause the computing system to: process agronomic input feature vectors to generate one or more predicted corn growth efficiency values; and provide the corn growth efficiency values as output. A computing system includes a processor; and one or more non-transitory, computer-readable storage media storing machine-readable instructions that, when executed by the one or more processors, cause the computing system to: process labeled agronomic data with a machine learning model to generate one or more predicted corn growth efficiency values; and modify a parameter of the machine learning model. A computer-implemented method includes processing labeled agronomic data with a machine learning model to generate corn growth efficiency values; and modifying a parameter of the machine learning model.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of application Ser. No. 17/739,699,entitled MACHINE LEARNING METHODS AND SYSTEMS FOR CHARACTERIZING CORNGROWTH EFFICIENCY, filed on May 9, 2022, which is incorporated herein byreference in its entirety.

TECHNICAL FIELD

The present disclosure is generally directed to characterizing corngrowth, and, more particularly, to machine learning (ML) methods andsystems for characterizing corn growth efficiency (CGE), and generatingfield management recommendations based on CGE values.

BACKGROUND

Growers, field managers, seed companies, and trusted advisors oftenstruggle to gain an understanding of the growing behavior of cornhybrids in agricultural fields. Conventionally used corn growthcharacteristics are often subjective and based on intuition, anecdote,or other unreliable and unreproducible information. Thus, conventionalcorn growth characteristics do not lend themselves to objective analysiswhen trying to understand which corn hybrids will grow well in whichfields and under which growing conditions. That is, conventional corngrowth characteristics do not lend themselves to objective performancequantification and, thus, may not be useful for comparing performanceswhen making corn hybrid selections and/or managements recommendations.Growers, field managers, seed companies, and trusted advisors are oftenunsure which corn hybrid to plant in which fields, and/or whatagricultural treatments to apply, which can be complicated by thevariability among different agricultural fields and growing conditions.

Thus, improved techniques for characterizing corn growth efficiency, andgenerating field management recommendations are needed.

BRIEF SUMMARY

In an aspect, a computing system includes one or more processors; andone or more non-transitory, computer-readable storage media storing: (i)a machine learning (ML) model trained using a training agronomic dataset corresponding to one or more trial agricultural fields, the trainingagronomic data set labeled with one or more known corn growth efficiency(CGE) values; and (ii) machine-readable instructions that, when executedby the one or more processors, cause the computing system to: (a)process one or more input feature vectors corresponding to an agronomicdata set with the ML model to generate one or more predicted CGE valuesfor one or more portions of an agricultural field; and (b) provide theone or more predicted CGE values as an output.

In another aspect, a computing system includes one or more processors;and one or more non-transitory, computer-readable storage media storingmachine-readable instructions that, when executed by the one or moreprocessors, cause the computing system to: (i) process an agronomic dataset with a machine learning (ML) model to generate one or more predictedcorn growth efficiency (CGE) values, the agronomic data set beinglabeled with one or more known CGE values; and (ii) modify one or moreparameters of the ML model.

In yet another aspect, a computer-implemented method includes (i)processing, using one or more processors, an agronomic data set with amachine learning (ML) model to generate one or more predicted corngrowth efficiency (CGE) values, the agronomic data set being labeledwith one or more known CGE values; and (ii) modifying, using one or moreprocessors, one or more parameters of the ML model.

BRIEF DESCRIPTION OF THE FIGURES

The figures described below depict various aspects of the system andmethods disclosed therein. It should be understood that each figuredepicts one embodiment of a particular aspect of the disclosed systemand methods, and that each of the figures is intended to accord with apossible embodiment thereof. Further, wherever possible, the followingdescription refers to the reference numerals included in the followingfigures, in which features depicted in multiple figures are designatedwith consistent reference numerals.

FIG. 1 is a block diagram of an example computing environment, accordingto an embodiment.

FIG. 2 is a block diagram of an example implementation of the corngrowth efficiency (CGE) determining module of FIG. 1 , according to anembodiment.

FIG. 3 is a flow diagram of an example computer-implemented method fortraining a machine learning (ML) model for predicting CGE values withinan agricultural field, according to an embodiment.

FIG. 4 is a flow diagram of an example computer-implemented method forimproving agricultural treatment application within an agriculturalfield, according to an embodiment.

FIG. 5 is a graph representing an example multi-genetics prescriptionmap, according to one embodiment and scenario.

FIG. 6 depicts an example chart for visualizing CGE values at growthstage R1 among different corn hybrids grown in different locations in afield, according to an embodiment.

FIG. 7 depicts an example chart for visualizing CGE values at growthstage R6 among different corn hybrids grown in different locations in afield, according to an embodiment.

FIG. 8 depicts an example chart for visualizing classifications oflocations based on CGE values at growth stages R1 and R6.

FIG. 9 depicts an example management recommendation.

FIG. 10 depicts another example management recommendation.

FIG. 11 is a graph of a portion of an example field includingindications of corn plant collection locations.

FIG. 12 depicts an example aerial vehicle configured to capture imagesof an agricultural field.

The figures depict embodiments for purposes of illustration only. One ofordinary skill in the art will readily recognize from the followingdiscussion that alternative embodiments of the systems and methodsillustrated herein may be employed without departing from the principlesof this disclosure.

DETAILED DESCRIPTION Overview

The present disclosure is generally directed to machine learning (ML)methods and systems for characterizing corn growth efficiency (CGE),and, more particularly, for generating field management recommendationsbased on one or more determined CGE values within a field, a sub-field,or any portion(s) thereof.

The present techniques advantageously improve the ability of individualsand organizations (e.g., a grower, a trusted advisor, a field manager, aseed company, etc.) that own and/or manage agricultural fields toobjectively measure or characterize corn plant growth in thoseagricultural fields at the field, sub-field, and/or portion(s) thereoflevel. In particular, the present techniques may be used to determinegrowth characteristics of corn plants to objectively analyze and informmanagement decisions and/or to assist in product placement.

Example objective characterizations of the growth and productivity of acorn plant (e.g., a stalk of corn) include corn growth efficiency (CGE)values. Disclosed example CGE values can be used to objectivelycharacterize or represent the growth efficiency of different cornhybrids at different growth stages in response to various environmentsin which the corn hybrids are growing. The environment can include, orbe represented by, any number and/or type(s) of environmental factorssuch as weather, soil properties, topography, agricultural treatments(e.g., fertilizer, nitrogen, fungicide, etc.) that were applied to cornplants at particular times, corn seed planting timing information, etc.For example, CGE values may be used to objectively characterize howparticular corn hybrids respond to drought conditions, hightemperatures, excess rain, early planting, late planting, etc.

Generally, example CGE values represent an amount of sucrose stored in acorn plant (i.e., a sucrose reserve). Disclosed CGE values can bedetermined at different growth stages to represent a growth efficiencyof a corn plant at the different growth stages. For example, a CGE valuedetermined at the silking growth stage (R1) represents the sucrosereserve that a corn plant has managed to build up, and which can laterbe kept as a stored reserve, converted to grow kernels of corn,allocated to grain, etc. Another example CGE value determined at thephysiological maturity growth stage (R6) represents how much of thesucrose reserve built up by R1 has been kept as a stored reserve, orconverted to grow kernels of corn, allocated to grain, etc. For clarityof description, “silking growth stage R1” will simply be referred toherein as “R1,” and “physiological maturity growth stage R6” will simplybe referred to herein as “R6.” In some examples, a high CGE value at R1is ideal (e.g., the sucrose reserve at R1 is large), and a low CGE valueat R6 is ideal (e.g., sucrose reserve at R6 is just enough to keep thecorn plant alive and upright), such that most of the sucrose reserve atR1 has been allocated to grain, converted to production of corn graindry matter, etc. Such CGE values represent a corn plant that has grownefficiently and vigorously in response to the environment in which itgrew, and then efficiently converted sucrose reserves into ears of corngrowth. In at least this way, CGE values can be used to objectivelycharacterize or represent the growth efficiency of different cornhybrids at different growth stages in response to various environmentsin which the corn hybrids are growing. However, CGE values can be usedin other ways, and/or can be computed at other growth stages toobjectively characterize corn growth efficiency.

An example sucrose reserve of a corn plant at a particular growth staget (e.g., at R1 or R6) can be expressed mathematically as:

SR _(t)=Σ_(i=6) ^(B)(C _(i) ×V _(i)),  EQN (1)

where V_(i) is a volume of liquid extracted from an internode i of thecorn plant; C_(i) is the concentration of sucrose in the extractedliquid; and i ranges from 6 to 8 (i.e., internodes 6 to 8 areconsidered).

In some examples, a sucrose reserve SR_(t) for a corn plant at a growthstage t is determined using a process that includes (i) collecting thecorn plant from a field, sub-field, or portion thereof at the growthstage t; (ii) squeezing each of internodes 6 to 8 of the corn plant toextract as much liquid as possible from each of the internodes; (iii)measuring the amount of liquid V_(i) extracted from each internode i;(iv) analyzing, for example, using a refractometer, to determine theconcentration of sucrose C_(i) in the liquid extracted from eachinternode i; and (v) computing the sucrose reserve SR_(t) using EQN (1).

In some examples, sucrose reserves determined for each of a plurality ofcorn plants collected from a field, sub-field, or portion(s) thereof atthe growth stage t are combined or averaged to compute an averagesucrose reserve SR_(t) for the field, sub-field, or portion(s) thereof(e.g., a hexagrid, as described below) at the growth stage t.Alternatively, the liquid squeezed from each of internodes 6 to 8 of theplurality of plants can be combined to form an aggregate liquid for eachof internodes 6 to 8, and the aggregate liquids can be measured andanalyzed to determine an average sucrose reserve SR_(t) for the field,sub-field, or portion(s) thereof at the growth stage t. The averagesucrose reserve SR_(t) at the growth stage t can be used, as describedbelow, to compute an average CGE value for the field, sub-field, orportion(s) thereof (e.g., a hexagrid, as discussed below) at the growthstage t.

In some examples, the above-described process is used to determinesucrose reserves SR_(t) for (i) a plurality of fields, sub-fields, orportions thereof (e.g., a plurality hexagrids); (ii) a plurality of cornhybrids; and/or (iii) a plurality of growing environments. Theseprocesses may be manual processes and/or automated processes (e.g., aprocess that utilizes one or more automated laboratory instruments orremotely sensed data). For example, a sampling probe can be used tomeasure sucrose and/or sucrose concentration.

An example CGE value EFCY_(R1) at R1 can be expressed mathematically as:

$\begin{matrix}{{{EFCY}_{R1} = {\frac{SR_{R1}}{\left( {{DM_{R1}} - {DM_{Vó}}} \right)}*DM_{R1}}},} & {{EQN}(2)}\end{matrix}$

where DM_(R1) is the total amount of dry plant matter in one or morecorn plants collected at R1; DM_(V6) is the total amount of dry plantmatter in one or more corn plants collected at growth stage V6; andSR_(R1) is the sucrose reserves of the one or more corn plants collectedat R1, which can be determined using EQN (1), for example.

In some examples, an amount of dry material DR_(t) for a corn plant at agrowth stage t is determined using a process that includes (i)collecting the corn plant from a field, sub-field, or portion thereof atthe growth stage t; (ii) separating the root from the stalk of the cornplant; (iii) drying the stalk; and (iv) weighing the dried stalk todetermine the amount of dry material DR_(t) for the corn plant.

In some examples, amounts of dry material determined for each of aplurality of corn plants collected from a field, sub-field, orportion(s) thereof at the growth stage t are combined to compute anaverage amount of dry material DR_(t) for the field, sub-field, orportion(s) thereof at the growth stage t. Average amounts of drymaterial DR_(t) can be used in EQN (2) to compute an average CGE valuefor the field, sub-field, or portion(s) (e.g., a hexagrid) thereof atR1.

In some examples, the above-described process is used to determineamounts of dry material DR_(t) for (i) a plurality of fields,sub-fields, or portions thereof (e.g., a plurality of hexagrids); (ii) aplurality of corn hybrids; and/or (iii) a plurality of growingenvironments. These processes may be manual processes and/or automatedprocesses (e.g., a process that utilizes one or more automatedlaboratory instruments). For example, an optical probe can be used todetermine mass. Additionally and/or alternatively, plant mass can bequantified by capturing and processing one or more multi-spectral imagesto determine amounts of plant material, such as the amounts of drymaterial DR_(t). For example, as shown in FIG. 12 , an unmanned aerialvehicle (e.g., a remotely operated drone 1204, a satellite, etc.) can beused to capture remotely-sensed multi-spectral images of corn plants1206 growing in one or more fields, sub-fields, and/or portion(s)thereof 1208. Additionally and/or alternatively, a manned aerial vehicle(e.g., a helicopter, balloon, plane, etc.) can be used to captureremotely-sensed multi-spectral images of corn plants growing in one ormore fields, sub-fields, and/or portion(s) thereof.

An example CGE value EFCY_(R6) at R6 can be expressed mathematically as:

EFCY_(R6) =SR _(R6),  EQN (3)

where SR_(R6) is the sucrose reserve of one or more corn plantscollected at R6, and can be determined using EQN (1), for example.

Hybrids with higher CGE values at particular locations or in particularenvironments may be better suited for the location(s) or environment(s)than other hybrids with lower CGE values at those locations orenvironments. Hybrids with lower CGE values may indicate an opportunityto increase yield through management practices that would increasesucrose reserves.

The present techniques may include analyzing one or more corn growthcharacteristics (e.g., CGE values) to identify one or more corn hybrids(e.g., varieties) to plant in one or more fields, sub-fields, orportion(s) thereof based on past, predicted, or anticipated growingenvironments associated with the fields, sub-fields, or portion(s)thereof. Additionally and/or alternatively, CGE values may be analyzedto determine management strategies (e.g., to determine a planting date,a plant population, a fertilizer timing, a fungicide timing, aninsecticide timing, harvesting time, etc.). For example, for a cornhybrid that has a low CGE at R1 (e.g., is poor at creating and storingsucrose), early fertilization may help increase yield. Additionallyand/or alternatively, by determining which agricultural treatments areeffective for certain hybrids in different growing environments, agrower, seed company, field manager, etc. may save money (e.g., inagricultural materials, fuel consumption, costs to acquire and applywater, etc.) by applying only those treatments that have been foundeffective for particular hybrids in particular growing environments.Corn growth characteristics may, additionally and/or alternatively, beused by, for example, a seed company, to rate and/or market corn hybridsfor different growing environments.

The present techniques may also include collecting machine data, anddetermining corn growth characteristics (e.g., CGE values) within one ormore agricultural fields by analyzing the machine data. In someembodiments, the corn growth characteristics are encoded in spatial datafiles encoded in a suitable file format, such as a commercial or opensource shapefile, a GeoJSON format, a Geography Markup Language (GML)file, etc. Such spatial data files may include one or more layers (i.e.,map layers, wherein each layer represents an agricultural characteristic(e.g., elevation, CGE values, etc.)). The individual layer(s) and/orfile(s) may be shared between multiple computing devices of anagricultural company, provided, or sold to customers, stored in adatabase, etc.

FIG. 1 is a block diagram of an example computing environment 100 inwhich methods, techniques, systems, and operations disclosed herein canbe implemented. The example environment 100 includes an example clientcomputing device 102, an example implement 104, an example remotecomputing device 106, and an example network 108. Some embodimentsinclude a plurality of client computing devices and/or a plurality ofimplements.

Example Client Computing Device

The example client computing device 102 can be any type of suitablepersonal computing device or system. For example, the client computingdevice 102 can be a mobile computing device (e.g., a mobile computingdevice, a smart phone, a tablet, a laptop, a wearable device, etc.). Insome examples, the client computing device 102 can be temporarily orpermanently coupled to the implement 104. The client computing device102 may be the property of a customer, an agricultural analytics (or“agrilytics”) company, an implement manufacturer, a field manager, aseed company, a retailer, a grower, a trusted advisor, etc.

The example client computing device 102 includes one or more processors110, memory 112, and a network interface controller (NIC) 114. Theprocessor(s) 110 may include any suitable number and/or type(s) ofprogrammable processors, such as microprocessors, microcontrollers,central processing units (CPUs), graphics processing units (GPUs),digital signal processor(s) (DSPs), etc. Generally, the processor(s) 110are configured to execute machine-readable instructions (e.g., software)stored in the memory 112. Additionally and/or alternatively, theprocessor(s) 110 may include logic circuits, such as a fieldprogrammable gate array (FPGA), an application specific integratedcircuit (ASIC), a hardware accelerator, a special-purpose computer chip,a system-on-a-chip (SoC) device, etc. that can implements methods,techniques, operations, etc. described herein without executingsoftware.

The memory 112 can include one or more volatile and/or non-volatiletangible or non-transitory memories (e.g., random access memory (RAM),read only memory (ROM), etc.), storage devices, storage disks, etc.(e.g., a hard disk drive, a flash memory, a compact disc, a digitalversatile disc, removable flash memory, etc.) accessible by theprocessor(s) 110 (e.g., via a memory controller) in which data ormachine-readable instructions can be stored for any suitable duration oftime (e.g., permanently, for an extended period of time (e.g., while aprogram associated with the machine-readable instructions is executing),and/or a short period of time (e.g., while the machine-readableinstructions are cached and/or during a buffering process)). Theprocessor(s) 110 interact with the memory 112 to obtain, for example,machine-readable instructions stored in the memory 112 corresponding toa data collection module 116, a mobile application module 118, and animplement control module 120, as described in more detail below, or,more generally, operations represented by the flowcharts of thisdisclosure. More or fewer modules may be included in the memory 112 insome embodiments.

The NIC 114 can include any suitable number and/or type(s) of networkinterface(s) to enable communication with other machines (e.g., theexample implement 104, the example remote computing device 106, etc.)via, for example, one or more networks (e.g., the network 108). Examplenetwork interface(s) include any suitable type of communicationinterface(s) (e.g., wired and/or wireless interfaces) configured tooperate in accordance with any suitable communication protocol(s).Example network interfaces include a TCP/IP interface, a WiFi™transceiver (e.g., according to the IEEE 802.11x family of standards),an Ethernet transceiver, a cellular network radio, a satellite networkradio, or any other suitable interface based on any other suitablecommunication protocols or standards. In some examples, the NIC 114 isexternal and communicatively coupled to the client computing device 102as a peripheral device.

The one or more modules stored in the memory 112 may include respectivesets of machine-readable instructions (e.g., software) implementingspecific functionality. For example, in an embodiment, the datacollection module 116 includes a set of machine-readable instructionsfor collecting machine data from an implement (e.g., the implement 104)for inclusion in an agronomic data set. The data collection module 116may include machine-readable instructions for collecting an above-groundand/or below-ground soil sample.

The machine data collection module 116 may include a respective set ofmachine-readable instructions for retrieving/receiving data from aplurality of different implements. For example, a first set ofinstructions may be for retrieving/receiving machine data from a firsttractor manufacturer's products, while a second set of instructions isfor retrieving/receiving machine data from a second tractormanufacturer's products. In another embodiment, the first and second setof instructions may be for, respectively, receiving/retrieving data fromtillage equipment and a harvester. Of course, some libraries ofmachine-readable instructions may be provided by the manufacturers ofvarious implements and/or attachments, and may be loaded into the memory112 and used by the data collection module 116. The data collectionmodule 116 may retrieve/receive machine data from a separate hardwaredevice (e.g., a client computing device 102 that is part of theimplement 104) or directly from one or more of the sensors of theimplement 104 and/or one or more of the attachments 130 coupled to theimplement 104, if any.

The machine data may include any information generated by the clientcomputing device 102, the implement 104, and/or the attachments 130. Insome cases, the machine data may be retrieved/received via the remotecomputing device 106 (e.g., from a third-party cloud storage platform).For example, the machine data may include values generated via a soilslaboratory or by analyzing a soil sample using a soil analysisattachment 130. The machine data may include sensor measurements ofengine load data, fuel burn data, draft, fuel consumption, wheelslippage, etc. The machine data may include one or more time series,such that one or more measured values are represented in a single dataset at a common interval (e.g., one-second). For example, the machinedata may include a first time series of draft at a one-second interval,a second time series of wheel slippage, etc.

The machine data may include location information. For example, theclient computing device 102 may add location metadata to the machinedata, such that the machine data reflects an absolute and/or relativegeographic position (e.g., location, coordinate, offset, heading, etc.)of the client computing device 102, the implement 104, and/or theattachments 130 within the agricultural field, sub-field, or portion(s)thereof at the precise moment that the client computing device 102captures the machine data. It will also be appreciated by those ofordinary skill in the art that some sensors and/or agriculturalequipment may generate machine data that is received by the clientcomputing device 102, and already includes location metadata added bythe sensors and/or agricultural equipment. In an embodiment, wherein themachine data comprises a time series, each value of the time series mayinclude a respective geographic metadata entry. It will be furtherappreciated by those of ordinary skill in the art that when the machinedata is received from a historical archive, the machine data may includehistorical location data (e.g., the global positioning system (GPS)coordinates corresponding to the location from which the historicalmachine data was captured).

The machine data collection module 116 may receive, access and/orretrieve the machine data via an application programming interface (API)through a direct hardware interface (e.g., via one or more wires) and/orvia a network interface (e.g., via the network 108). The data collectionmodule 116 may collect (e.g., pull the machine data from a data sourceand/or receive machine data pushed by a data source) at a predeterminedtime interval. The time interval may be of any suitable duration (e.g.,once per second, once or twice per minute, every 10 minutes, etc.). Thetime interval may be short, in some embodiments (e.g., once every 10milliseconds). The data collection module 116 may includemachine-readable instructions for modifying and/or storing the machinedata. For example, the data collection module 116 may parse the rawmachine data into a data structure. The data collection module 116 maywrite the raw machine data onto a disk (e.g., a hard drive in the memory112).

In some embodiments, the machine data collection module 116 may transferthe raw machine data, or modified machine data, to a remote computingsystem/device, such as the remote computing device 106. The transfermay, in some embodiments, take the form of an SQL insert command. Ineffect, the data collection module 116 performs the function ofreceiving, processing, storing, and/or transmitting the machine data.The data collection module 116 may receive (e.g., from a soil probeattachment) soil sample data corresponding to one or more points withinthe machine data.

The mobile application module 118 may include machine-readableinstructions (e.g., software) that display one or more graphical userinterfaces (GUIs) on one or more output devices 126 and/or receive userinput via one or more input devices 124. For example, the mobileapplication module 118 may correspond to a mobile computing application(e.g., an Android, iPhone, or other) computing application of anagrilytics company. The mobile computing application may be aspecialized application corresponding to the type of computing deviceembodied by the client computing device 102. For example, in embodimentswhere the client computing device 102 is a mobile phone, the mobileapplication module 118 may correspond to a mobile application downloadedfor the mobile phone. When the client computing device 102 is a tablet,the mobile application module 118 may correspond to an application withtablet-specific features. Example GUIs that may be displayed by themobile application module 118, and with which the user may interact,display or present location-aware planting and/or agricultural treatmentrecommendations on a per hexagrid basis.

The mobile application module 118 may include machine-readableinstructions for receiving/retrieving mobile application data from theremote computing device 106. In particular, the mobile applicationmodule 118 may include machine-readable instructions for transmittinguser-provided login credentials, receiving an indication ofsuccessful/unsuccessful authentication, and other functions related tothe user's operation of the mobile application. The mobile applicationmodule 118 may include machine-readable instructions for receiving,accessing, retrieving, rendering, and/or displaying visual maps in aGUI. An example visual map represents location-aware planting and/oragricultural treatment recommendations on a per hexagrid basis.Specifically, the application module 118 may include machine-readableinstructions for displaying one or more map layers in the outputdevice(s) 126 of the client computing device 102.

The implement control module 120 includes machine-readable instructionsfor controlling the operation of an implement (e.g., the implement 104)and/or the attachments 130. The implement control module 120 may controlthe implement 104 while the implement 104 and/or attachments 130 are inmotion (e.g., while the implement 104 and/or attachments 130 are beingused in a farming capacity). For example, the implement control module120 may include an instruction that, when executed by the processor 110of the client computing device 102, causes the implement 104 toaccelerate or decelerate, collect a soil sample using a soil probe, orchange hybrids on a planter.

In some embodiments, the implement control module 120 may cause one ofthe attachments 130 to raise or lower the disc arm of tillage equipment,or to apply more or less downward or upward pressure on the ground. Insome embodiments, the implement control module 120 may control theattachments 130 in response to a predicted CGE value corresponding tothe agricultural field where the implement 104 is positioned. Forexample, the implement control module 120 may access known or predictedCGE values for the field, and use the CGE values to automaticallydetermine a corn hybrid to plant and/or an agricultural treatment toapply, and then automate the attachments 130 to plant the corn hybridand/or apply the agricultural treatment. Practically, the implementcontrol module 120 has at least as much control of the implement 104and/or attachments 130 as does the human operator.

The implement control module 120 may include a respective set ofmachine-readable instructions for controlling a plurality of implements.For example, a first set of instructions may be for controlling animplement of a first tractor manufacturer, while a second set ofinstructions is for controlling an implement of a second tractormanufacturer. In another embodiment, the first and second set ofinstructions may be for, respectively, controlling a tiller and aharvester. Of course, many configurations and uses are envisioned beyondthose provided by way of example.

In some embodiments, the implement control module 120 may includemachine-readable instructions for executing one or more agriculturalprescriptions with respect to a field. For example, the control module120 may execute an agricultural prescription that specifies, for a givenagricultural field, a varying application rate of a chemical (e.g., afertilizer, an herbicide, a pesticide, etc.) or the hybrid to plant atvarious points along the path based on the clay characteristics of thefield. The control module 120 may analyze the current location of theimplement 104 and/or the attachments 130 in real-time (i.e., as thecontrol module 120 executes the agricultural prescription).

In some embodiments, one or more components of the computing device 102may be embodied by one or more virtual instances (e.g., a cloud-basedvirtualization service). In such cases, one or more client computingdevice 102 may be included in a remote data center (e.g., a cloudcomputing environment, a public cloud, a private cloud, etc.). Forexample, a remote data storage module (not depicted) may remotely storedata received/retrieved by the computing device 102. The clientcomputing device 102 may be configured to communicate bidirectionallyvia the network 108 with the implement 104 and/or an attachment 130 thatmay be coupled to the implement 104. The implement 104 and/or theattachments 130 may be configured for bidirectional communication withthe client computing device 102 via the network 108.

The client computing device 102 may receive/retrieve data (e.g., machinedata) from the implement 104, and/or the client computing device 102 maytransmit data (e.g., instructions) to the implement 104. The clientcomputing device 102 may receive/retrieve data (e.g., machine data) fromthe attachments 130, and/or may transmit data (e.g., instructions) tothe attachments 130. The implement 104 and the attachments 130 will nowbe described in further detail.

The implement 104 may be any suitable powered or unpoweredequipment/machine or machinery, including without limitation: a tractor,a combine, a cultivator, a cultipacker, a plow, a harrow, a stripper, atiller, a planter, a baler, a sprayer, an irrigator, a sorter, aharvester, etc. The implement 104 may include one or more sensors (notdepicted) including one or more soil probe and the implement 104 may becoupled to one or more attachments 130. For example, the implement 104may include one or more sensors for measuring respective implementvalues of engine load data, fuel burn data, draft sensing, fuelconsumption, wheel slippage, etc. Many embodiments including more orfewer sensors measuring more or fewer implement values are envisioned.The implement 104 may be a gas/diesel, electric, or hybrid vehicleoperated by a human operator and/or autonomously (e.g., as anautonomous/driverless agricultural vehicle).

The attachments 130 may be any suitable powered or unpoweredequipment/machinery permanently or temporarily affixed/attached to theimplement 104 by, for example, a hitch, yoke or other suitablemechanism. The attachments 130 may include any of the types of equipmentthat the implement 104 may comprise (e.g., field cultivator, disc,planter). The attachments 130 may include one or more sensors (notdepicted) that may differ in number and/or type according to therespective type of the attachments 130 and the particularembodiment/scenario. For example, a tiller attachment 130 may includeone or more soil coring probes. It should be appreciated that manyattachments 130 sensor configurations are envisioned. For example, theattachments 130 may include one or more cameras. The attachments 130 maybe connected to the implement 104 via wires or wirelessly, for bothcontrol and communications. For example, attachments 130 may be coupledto the client computing device 102 of the implement 104 via a wiredand/or wireless interface for data transmission (e.g., IEEE 802.11,WiFi, Bluetooth®, universal serial bus (USB), etc.) and main/auxiliarycontrol (e.g., 7-pin, 3-pin, etc.). The client computing device 102 maycommunicate bidirectionally (i.e., transmit data to, and/or receive datafrom) with the remote computing device 106 via the network 108.

The client computing device 102 includes the input device(s) 124 andoutput device(s) 126. The input device(s) 124 may include any suitabledevice or devices for receiving input, such as one or more microphone,one or more camera, a hardware keyboard, a hardware mouse, a capacitivetouch screen, etc. The output device(s) 126 may include any suitabledevice for conveying output, such as a hardware speaker, a computermonitor, a touch screen, etc. In some cases, the input device(s) 124 andthe output device(s) 126 may be integrated into a single device, such asa touch screen device that accepts user input and displays output. Theclient computing device 102 may be associated with (e.g., leased, owned,and/or operated by) an agrilytics company.

The network 108 may be a single communication network, or may includemultiple communication networks of one or more types (e.g., one or morewired and/or wireless local area networks (LANs), and/or one or morewired and/or wireless wide area networks (WANs) such as the Internet).The network 108 may enable bidirectional communication between theclient computing device 102 and the remote computing device 106, orbetween multiple client computing devices 102, for example.

Example Remote Computing Device

The remote computing device 106 includes one or more processors 140,memory 142, and a NIC 144. The processor(s) 140 may include any suitablenumber and/or type(s) of programmable processors, such asmicroprocessors, microcontrollers, CPUs, GPUs, DSPs, etc. Generally, theprocessor(s) 140 are configured to execute machine-readable instructions(e.g., software) stored in the memory 142. Additionally and/oralternatively, the processor(s) 140 may include logic circuits, such asan FPGA, an ASIC, a hardware accelerator, a special-purpose computerchip, a SoC device, etc. that can implements methods, techniques,operations, etc. described herein without executing software.

The memory 142 may include one or more volatile and/or non-volatiletangible or non-transitory memories (e.g., RAM, ROM, etc.), storagedevices, storage disks, etc. (e.g., a hard disk drive, a flash memory, acompact disc, a digital versatile disc, removable flash memory, etc.)accessible by the processor(s) 140 (e.g., via a memory controller) inwhich data or machine-readable instructions may be stored for anysuitable duration of time (e.g., permanently, for an extended period oftime (e.g., while a program associated with the machine-readableinstructions is executing), and/or a short period of time (e.g., whilethe machine-readable instructions are cached and/or during a bufferingprocess)). The processor(s) 140 interact with the memory 142 to obtainand execute, for example, machine-readable instructions stored in thememory 142 corresponding to a data processing module 150, a topographicmodule 152, a CGE determining module 154, a prescription module 156, asdescribed in more detail below, or, more generally, operationsrepresented by the flowcharts of this disclosure. More or fewer modulesmay be included in the memory 142, some embodiments.

The NIC 144 may include any suitable number and/or type(s) of networkinterface(s) to enable communication with other machines (e.g., theexample client computing device 102, etc.) via, for example, one or morenetworks (e.g., the network 108). Example network interface(s) includeany suitable type of communication interface(s) (e.g., wired and/orwireless interfaces) configured to operate in accordance with anysuitable communication protocol(s). Example network interfaces include aTCP/IP interface, a WiFi™ transceiver (e.g., according to the IEEE802.11x family of standards), an Ethernet transceiver, a cellularnetwork radio, a satellite network radio, or any other suitableinterface based on any other suitable communication protocols orstandards. In some examples, the NIC 144 is external and communicativelycoupled to the client computing device 102 as a peripheral device.

In an embodiment, the data processing module 150 includesmachine-readable instructions for receiving/retrieving data from theclient computing device 102, the implement 104, and/or the attachments130. For example, the data processing module 150 may includemachine-readable instructions that when executed by the processor 140,cause the remote computing device 106 to receive/access/retrieveagronomic data that may include machine data, for example. The dataprocessing module 150 may include further machine-readable instructionsfor storing the agronomic data in one or more tables of a database 180.The data processing module 150 may store raw agronomic data, orprocessed data.

The data processing module 150 may include machine-readable instructionsfor processing the raw agronomic data to generate processed data. Forexample, the processed data may be data that is represented using datatypes data of a programming language (e.g., R, C #, Python, JavaScript,etc.). The data processing module 150 may include machine-readableinstructions for validating the data types present in the processeddata. For example, the data processing module 150 may verify that avalue is present (i.e., not null) and is within a particular range or ofa given size/structure. In some embodiments, the data processing module150 may transmit processed data from the database 180 in response to aquery, or request, from the client computing device 102. The dataprocessing module 150 may transmit the processed data via HTTP or viaanother data transfer suitable protocol.

In some examples, the data processing module 150 of FIG. 1 includes aset of computer-executable instructions for analyzing remotely-sensedimagery (e.g., high-resolution visible and/or near-infrared (VNIR)imagery) to estimate plant physiological properties such as amounts ofplant material and/or amounts of dry plant material. In some examples,such as that shown in FIG. 12 , an unmanned aerial vehicle (e.g., aremotely operated drone 1204, a satellite, etc.) can be used to captureremotely-sensed multi-spectral images of corn plants 1206 growing in oneor more fields, sub-fields, and/or portion(s) thereof 1208. Additionallyand/or alternatively, a manned aerial vehicle (e.g., a helicopter,balloon, plane, etc.) can be used to capture remotely-sensedmulti-spectral images of corn plants growing in one or more fields,sub-fields, and/or portion(s) thereof. The data processing module 150may include further computer-executable instructions for analyzing theplant physiological properties to compute one or more CGE values.Specifically, the data processing module 150 can include instructionsfor extracting one or more combinations of spectral bands (e.g., one ormore vegetation indices, one or more derivative spectroscopy values,etc.) from the remotely-sensed imagery, and analyze the combinations ofspectral bands to predict CGE values.

The topographic module 152 may include machine-readable instructions forretrieving, accessing, and/or providing mapping data (e.g., electronicmap layer objects) to other modules in the remote computing device 106.The mapping data may take the form of raw data. In some embodiments, thetopographic module 152 may include spatial data files. The topographicmodule 152 may store mapping data in, and retrieve mapping data from,the database 180. The topographic module 152 may source elevation datafrom public sources, such as the United States Geological Survey (USGS)National Elevation Dataset (NED) database. In some embodiments, the dataprocessing module 150 may provide raw data to the topographic module152, wherein machine-readable instructions within the topographic module152 infer the elevation of a particular tract of land by analyzing theraw data. The elevation data may be stored in a two-dimensional (2D) orthree-dimensional (3D) data format, depending on the embodiment andscenario.

Example Corn Growth Efficiency (CGE) Value Determination

The CGE determining module 154 may process an agronomic data set (e.g.,stored in the database 180) to predict one or more CGE valuescorresponding to one or more fields, sub-fields, and/or portion(s)thereof. In some embodiments, fields, sub-fields, and/or portion(s)thereof are divided into a grid of hexagonal cells, called “hexagrids”herein. In some embodiments, the hexagrids are 8.5 meters across. Insome embodiments, the CGE determining module 154 may process theagronomic data set to train and operate a machine learning (ML) model,as described below with respect to FIG. 2 .

Example agronomic data that may be included in an agronomic data setincludes machine data (e.g., as described above), topographic data(e.g., as described above), information related to one or moreagricultural treatments applied to fields, sub-fields, and/or portion(s)thereof, information related to dates when agricultural treatments wereapplied, information related to which corn hybrids were planted in whichfields, sub-fields, and/or portion(s) thereof, information related towhen fields, sub-fields, and/or portion(s) thereof were planted, weatherinformation for the fields, sub-fields, and/or portion(s) thereof, etc.However, any other data related to a growing environment may be includedin the agronomic data set.

FIG. 2 is a block diagram of an example CGE determining module 200 thatmay be used to implement the CGE determining module 154 of FIG. 1 . TheCGE determining module 200 may include machine-readable instructions fortraining and operating one or more ML models 202 to predict one or moreCGE values 204 corresponding to an agricultural field, sub-field and/orportion(s) thereof based on input vectors of agronomic data 205 (e.g.,data collected and/or processed by the data processing module 150) orvalues determined from the agronomic data 205 (e.g., an average, etc.).Example ML models 202 include an artificial neural network, amultinomial logistic regression model, a decision tree, a gradientboosting model, a random forest model, a logistic regression model, etc.The predicted CGE values 204 may be associated with one or morehexagrids.

The block diagram includes a data transformer 208 that generates one ormore input data vectors 206 for the ML model 202 by transforming theagronomic data 205. Specifically, the data transformer 208 may includemachine-readable instructions for processing the agronomic data 205 togenerate input feature vectors 206 for the ML model 202. For example,the data transformer 208 may compute averages, remove noise, convertunits, compute a value of a first type from a value of a second type,compute a value or index based on one or more physical measurements,etc. An example input feature vector 206 for the ML model 202 includesone or more of soil topography, relative elevation, slope, latitude,growing season length, incident solar radiation, phosphorus fertility,potassium fertility, soil wetness index (SWI), organic matter, CEC,planting date, corn hybrid planted, applied agricultural treatments,treatment dates, weather information, environmental stresses, etc. withrespect to one or more locations within a field, sub-field, orportion(s) thereof.

For example, the input feature vectors 206 may be labeled with thegeographic coordinates of a respective hexagrid corresponding to theinput values and, when known, respective CGE values determined byprocessing one or more plants collected from within the hexagrid. Forexample, a field may include a plurality of hexagrids, wherein eachhexagrid includes a respective plurality of corn plants. The presenttechniques may include determining a respective CGE value for each ofthe corn plants, and assigning the respective CGE value to eachrespective plant. In some cases, CGE values may be aggregated at thehexagrid level.

The present techniques may determine the respective CGE value using amanual process and/or an automated process. Specifically, one or morehumans may manually process corn plants as described above, and enterthe information into a computer, associating each plant's CGE value withthe agronomic data 205. In other embodiments, an implement may generatemachine data, wherein the generated machine data includes point dataincluding respective CGE values for each point in an agricultural field.In this way, the present techniques may analyze an individualfield/sub-field (e.g., one or more hexagrids) to identify individualcorn plants, the respective CGE value of each of the plants, and/orrespective CGE values across the hexagrid(s).

Returning to FIG. 2 , the CGE determining module 200 may train the MLmodels 202 by implementing a comparer 210. When the CGE determiningmodule 200 is training the ML models 202, the data transformer 208 maygenerate input feature vectors 206 using the agronomic data 205 thatinclude known CGE values 212 (e.g., wherein the CGE values 212 weremanually determined, as described above). In this way, the agronomicdata 205 may be labeled data, and the data transformer 208 may preservesuch labels when transforming the agronomic data 205. Specifically, eachinput feature vector 206 may include a respective label corresponding tothe CGE value of each respective input vector 206. Training the ML model202 may include repeatedly inputting labeled data and evaluating a lossfunction until the loss moves toward a minima (e.g., until the modelconverges). The ML models 202 may process each vector of input data 206to learn to predict the one or more CGE values 204.

In general, the present techniques may train the ML models 202 by, interalia, establishing a network architecture, or topology, and adding oneor more layers that may be associated with one or more respectiveactivation functions (e.g., a rectified linear unit, softmax, etc.),loss functions and/or optimization functions. Multiple different typesof artificial neural networks may be employed, including withoutlimitation, recurrent neural networks, convolutional neural networks,and deep learning neural networks. Data sets used to train theartificial neural network(s) may be divided into training, validation,and testing subsets; these subsets may be encoded in an N-dimensionaltensor, array, matrix, or other suitable data structures. Training maybe performed by iteratively training the network using labeled trainingsamples (e.g., training samples labeled using one or moreobserved/measured CGE values). Training of the artificial neural networkmay produce byproduct weights, or parameters which may be initialized torandom values. The weights may be modified as the network is iterativelytrained, by using one of several gradient descent algorithms, to reduceloss and to cause the values output by the network to converge toexpected, or “learned,” values.

In an embodiment, a regression neural network may be selected whichlacks an activation function, wherein input data may be normalized bymean centering, to determine loss and quantify the accuracy of outputs.Such normalization may use a mean squared error loss function and meanabsolute error. The artificial neural network model may be validated andcross-validated using standard techniques such as hold-out, K-fold, etc.In some embodiments, multiple artificial neural networks may beseparately trained and operated, and/or trained and operated inconjunction. The ML models 202 may include machine-readable instructionsexecuted by a processor or a processing element using supervised orunsupervised machine learning, and the machine learning module mayemploy a neural network, which may be a convolutional neural network, adeep learning neural network, or a combined learning module or programthat learns in two or more fields or areas of interest. Machine learningmay involve identifying and recognizing patterns in existing data inorder to facilitate making predictions for subsequent data. Models maybe created based upon example inputs in order to make valid and reliablepredictions for novel inputs. For example, a deep learning artificialneural network may be trained using historical machine data togeneralize about previously unseen machine data. The ML models 202 maystore one or more trained ML models in a memory and/or in an electronicdatabase. The ML models 202 may transmit trained ML models to anothercomponent of the computing environment 100 (e.g., the client computingdevice 102).

In particular, the comparer 210 may compute differences 214 between theknown CGE values 212 and the corresponding predicted CGE values 204, andthe ML model 202 updates one or more of its parameters (e.g.,coefficients, weights, etc.) based upon the differences 214 using, forexample, predictive modeling. The process may be repeated until astatistical measure (e.g., root mean square (rms), least squares,quadratic loss, etc.) of the differences 214 satisfies a predeterminedthreshold. The comparer 210 may use a gradient descent optimizationalgorithm to minimize the differences 214.

In operation, the CGE determining module 200 may be used to infer orpredict one or more CGE values for a field, sub-field, or portion(s)thereof that was not used to train the CGE determining module 200. Thatis, the CGE determining module 200 may train the one or more ML models202 using labeled data as described above. Next, agronomic data 205corresponding to an agricultural field may be collected, as describedabove. The agronomic data 205 may be transformed by the data transformer208 to generate input feature vectors 206. The trained ML models 202 mayprocess the input feature vectors 206 to determine one or morerespective predicted CGE values 204.

Returning to FIG. 1 , the prescription module 156 includesmachine-readable instructions for generating one or more agriculturalprescriptions. The agricultural prescriptions may be a set ofinstructions for performing one or more agricultural interventions withrespect to an agricultural field. For example, the agriculturalprescription may include one more map layers specifying a respective setof interventions relating to seeding, fertilization, tillage, etc. Theclient computing device 102 may receive/retrieve the prescriptioninstructions, and execute them.

The prescription module 156 may include computer-executable instructionsthat when executed, cause one or more agricultural prescriptions, orscripts to be generated. The agricultural prescriptions may includemachine-readable instructions for causing an implement (e.g., theimplement 104) to perform one or more tasks (e.g., instruct a planter toswitch from product A to product B). The prescription may includeinstructions causing the implement 104 to perform a task in apredetermined way (e.g., plant specific seeds at a specific rate and ata specific location) when 1) the location of the implement 104 coincideswith a particular CGE value; and 2) the location of the implement 104coincides with a particular field, as determined by reference to thefield map layer. In this way, the prescription module 156 may generateprescriptions executable by a client device for modifying how seeds areplanted, treated, or otherwise managed.

In some examples, the computing device 102 may include instructions(e.g., in the mobile app 118) that, when executed, cause one or more ofthe prescriptions generated by the prescription module 156 to bedownloaded into the memory 112 of the client computing device 102. Theinstructions may further cause the client computing device 102 tocontrol the implement 104 in order to carry out the specificinstructions of the downloaded prescription, such that, for example, theclient computing device 102 is caused to perform an agriculturalmanagement operation in response to a CGE value at a particular locationwithin the field. For example, the client computing device 102 mayreceive location information from a location module (not depicted) suchas a GPS module of the client computing device 102. When the implement104 is being operated over a particular location (e.g., a hexagrid)having a particular CGE value, the instructions of the prescription maycause an amount of fertilizer to be dispensed by the attachment 130 ofthe implement 104, and/or a particular seed hybrid to be deposited. Theamount of fertilizer to be dispensed, or the hybrid to be planted inthis example may be determined by the ML model 202 of FIG. 2 , forexample. It will be appreciated by those of ordinary skill in the artthat many other agricultural management operations are envisioned.

The database 180 may be implemented as a relational database managementsystem (RDBMS), in some embodiments. For example, the database 180 mayinclude one or more structured query language (SQL) databases, a NoSQLdatabase, a flat file storage system, or any other suitable data storagesystem/configuration. In general, the database 180 allows the clientcomputing device 102 and/or the remote computing device 106 to create,retrieve, update, and/or retrieve records relating to performance of thetechniques herein. For example, the database 180 may allow the clientcomputing device 102 to store information received from one or moresensors of the implement 104 and/or the attachments 130. The database180 may include a Lightweight Directory Access Protocol (LDAP)directory, in some embodiments. The client computing device 102 mayinclude a module (not depicted) including a set of machine-readableinstructions for querying an RDBMS, an LDAP server, etc. For example,the client computing device 102 may include a set of database driversfor accessing the database 180 of the remote computing device 106. Insome embodiments, the database 180 may be located remotely from theremote computing device 106, in which case the remote computing device106 may access the database 180 via the NIC 114 and the network 108.

The remote computing device 106 may further include one or more inputdevices 182, and one or more output devices 184. The input device(s) 182may include any suitable device or devices for receiving input, such asone or more microphones, one or more cameras, a hardware keyboard, ahardware mouse, a capacitive touch screen, etc. The input device(s) 182may allow a user (e.g., a system administrator) to enter commands and/orinput into the remote computing device 106, and to view the result ofany such commands/input in the output device(s) 184. For example, anemployee of the agrilytics company may use the input device 182 toadjust parameters with respect to one or more agricultural fields forplanting, applying treatments, etc.

The output device(s) 184 may include any suitable device for conveyingoutput, such as a hardware speaker, a computer monitor, a touch screen,etc. The remote computing device 106 may be associated with (e.g.,leased, owned, and/or operated by) an agrilytics company. The remotecomputing device 106 may be implemented using one or more virtualizationand/or cloud computing services. One or more application programminginterfaces (APIs) may be accessible by the remote computing device 106.

In operation, the agrilytics company may access the remote computingdevice 106 to establish one or more field records on behalf of one ormore growers. For example, the company may store the field records inthe database, wherein each grower is associated with a unique identifier(e.g., a universally unique identifier (UUID)) as are each of thegrower's respective fields. For example, the grower may be associatedwith the grower's fields in the database via a one-to-many relationship.

The agrilytics company may populate the database 180 with agronomic datacorresponding to the grower's fields by using the implement 104 to drivethe fields and collect machine data and/or other growing environmentdata. The machine data may include information gathered from anattachment 130 (e.g., a soil probe) and/or machine data collected fromother sources. The agrilytics company may additionally populate thedatabase 180 with CGE values corresponding to the grower's fields bysampling and processing corn plants, as described above. Once theagronomic data and CGE values for the grower's fields have beencollected, the CGE determining module 154 may process the agronomic datato determine predicted CGE values. The CGE values may be assigned to oneor more hexagrids within the field.

The prescription module 156 may include machine-readable instructionsthat analyze the CGE values of the field and determine one or moretreatments for affecting portions of the field. For example, theprescription module 156 may be pre-programmed to switch corn varieties(e.g., corn hybrids) when specific environmental characteristics (i.e.,soil, topography) change within a hexagrid or combination of hexagrids.The instructions for switching corn varieties may vary based on thepredicted CGE values of the hexagrid. It should be appreciated that theexamples provided are intentionally simplified for explanation, and manyfurther embodiments are envisioned, as described below.

While the CGE determining module 154 and prescription module 156 aredescribed with reference to the remote computing device 106, the CGEdetermining module 154, prescription module 156 and/or portion(s)thereof may be implemented by the client computing device. For example,the remote computing device 106 may implement the CGE determining module154 to train a machine learning model (e.g., the model 202) that issubsequently used by a CGE determining module 154 implemented by theclient computing device 102 to process de novo agronomic data to predictCGE values that are used by a prescription module 156 implemented by theclient computing device 102 to develop agricultural prescriptions.

Example Computer-Implemented Methods

FIG. 3 depicts a flow diagram of an example computer-implemented method300 for training a ML model (e.g., the model 202) to predict one or moreCGE values within an agricultural field, according to one embodiment.The method 300 may be implemented as an executable program or portion ofan executable program for execution by a processor such as one of theprocessors 110, 140 of FIG. 1 . The program may be embodied inmachine-readable instructions stored on a non-transitory or tangiblemachine-readable storage medium such as a compact disc (CD), hard diskdrive (HDD), digital versatile disk (DVD), Blu-ray disk, cache, flashmemory, read-only memory (ROM), random access memory (RAM), or any otherstorage device or storage disk associated with the processor 110, 140 inwhich information may be stored for any duration (e.g., for extendedtime periods, permanently, for brief instances, for temporarilybuffering, and/or for caching of the information). The order ofexecution of the blocks of FIG. 3 may be changed, and/or some of theblocks described may be changed, eliminated, or combined. Additionally,or alternatively, any or all of the blocks may be implemented by one ormore of a hardware circuit (e.g., discrete and/or integrated analogand/or digital circuitry), ASIC, FPGA, etc. structured to perform thecorresponding operation(s) without executing software or instructions.

The method 300 may include accessing a training agronomic data setcorresponding to one or more trial agricultural fields (block 302). Atrial agricultural field may be a field from which corn plants arecollected and processed to determine known CGE values, and for whichagronomic data is collected that is used to train an ML model forpredicting CGE values. The training agronomic data set may includelabeled data corresponding to the trial agricultural field(s) (e.g., oneor more measurements taken using a soil probe corresponding to soilvalues, as described above). In some embodiments, the training agronomicdata set may include historical agronomic data collected previously. Thetraining agronomic data may be collected by the implement 104, oranother process/actor, in some embodiments. The training agronomic datamay also be collected from other databases (e.g., a historical weatherdatabase) or other sources. The method 300 may include accessing knownCGE values corresponding to the trial agricultural field(s) (block 304).For example, the method 300 may access a respective CGE value for eachof a plurality of hexagrids.

In some embodiments, the method 300 may include labeling the trainingagronomic data with the known CGE values (block 306). The method 300 mayinclude processing the labeled agronomic data with the ML model 202 totrain one or more ML models to generate predicted CGE valuescorresponding to the known CGE values (block 308). The method 300 mayinclude computing differences between the known CGE values and thepredicted CGE values (block 310). The method 300 may include updatingone or more parameters (e.g., coefficients, weights, etc.) of the MLmodel 202 based upon the differences (block 312). The method 300 mayinclude repeating blocks 308-412 until a statistical measure (e.g., rms,least squares, etc.) of the differences 214 satisfies a predeterminedthreshold. The method 300 may include storing the trained ML modelsand/or the weights/parameters of each of the respective trained MLmodels in a non-transitory memory and/or an electronic database forlater use (e.g., to predict one or more CGE values corresponding to anagricultural field, an agricultural sub-field, a hexagrid, etc.).

FIG. 4 depicts a flow diagram of an example computer-implemented method400 for improving agricultural treatment application within anagricultural field, according to one embodiment and scenario. The method400 may be implemented using an executable program or portion of anexecutable program for execution by a processor such as one of theprocessors 110, 140 of FIG. 1 . The program may be embodied in softwareand/or machine-readable instructions stored on a non-transitory ortangible machine-readable storage medium such as a CD, HDD, DVD, Blu-raydisk, cache, flash memory, ROM, RAM, or any other storage device orstorage disk associated with the processor 110, 140 in which informationmay be stored for any duration (e.g., for extended time periods,permanently, for brief instances, for temporarily buffering, and/or forcaching of the information). The order of execution of the blocks ofFIG. 4 may be changed, and/or some of the blocks described may bechanged, eliminated, or combined. Additionally, or alternatively, any orall of the blocks may be implemented by one or more of a hardwarecircuit (e.g., discrete and/or integrated analog and/or digitalcircuitry), ASIC, PLD, FPGA, FPLD, logic circuit, etc. structured toperform the corresponding operation(s) without executing software orinstructions.

The method 400 may include collecting a production agronomic machinedata set corresponding to one or more target agricultural field(s)(block 402). A target agricultural field may be a field for which anagricultural prescription is to be determined and applied based onpredicted CGE values. The production agronomic data set may includelabeled data corresponding to the trial agricultural field(s) (e.g., oneor more measurements taken using a soil probe corresponding to soilvalues, as described above). In some embodiments, the productionagronomic data set may include historical agronomic data collectedpreviously for the target agricultural field(s). The productionagronomic data may be collected by the implement 104, or anotherprocess/actor, in some embodiments. The production agronomic data mayalso be collected from other databases (e.g., a historical weatherdatabase) or other sources.

The method 400 may include analyzing the production agronomic data setwith the one or more trained ML models 202 to predict one or more CGEvalues corresponding to one or more respective hexagrids located withinthe target agricultural field(s) (block 404). As noted above, thecollection and analysis of production agronomic data may be performed bythe client computing device 102 and/or the remote computing device 106.In either case, the method 400 may annotate each data point within theproduction agronomic data with a geographic position (e.g., a hexagrididentifier). The geographic position of each point may be added to theproduction agronomic data upon collection by an implement and/or acomputing device (e.g., by an onboard Global Positioning System (GPS)device of the implement 104 or the client computing device 106).

The method 400 may associate each hexagrid with the predicted CGEvalues, such that once the method 400 has been completed, each of thehexagrids within the target field(s) includes one or more respective CGEvalues corresponding to the individual corn plants and/or varietieslocated within that hexagrid. In this way, the grower, field manager,trusted advisor or other relevant party can advantageously gain anobjective understanding of how different regions of the target field(s)and/or growing or environmental conditions are influencing CGE (e.g., byviewing a field map layer showing respective CGE values for the field).The ability to view differing CGE values across an entire field orsubfield is advantageous for practical growing purposes. For example, agrower operating the implement 104 of FIG. 1 may view the field maplayer including CGE values and initiate prescriptive fungicideapplications in areas where corn plants are more likely to haveadditional growth (e.g., have higher CGE values) in connection with ahigher yield environment.

As discussed above, the method 400 may include generating andtransmitting (e.g., from the remote computing device 106) one or moremap layers via the network 108 for display in the client computingdevice 106. The one or more map layers may include the predicted CGEvalues, in some embodiments. In still further embodiments, the presenttechniques may be used, optionally in conjunction with other non-CGEcharacteristics map layers, to automate the application of agriculturaltreatments. For example, the method 400 may include generating anagricultural prescription for the agricultural field, including at leastone treatment based on the CGE values (block 406). An exampleprescription includes variety selection, seeding rate, field plantingpriority, in season management including fertility (e.g., macro ormicronutrients spread or foliar sprayed), crop protection (e.g.,fungicide, insecticide), biologicals, etc.

However, the present techniques represent a further advantageousimprovement over conventional techniques that require the grower tomaintain constant attention during the laborious planting and harvestseasons, which may be further challenging due to hot/cold weather,precipitation and, in many cases, working in darkness. To that end, themethod 400 may include performing the generated agriculturalprescription by, for example, transmitting the prescription in the formof an electronic prescription file to the client computing device 102for execution in the implement control module 120 (block 408). Theagricultural prescription may include sets of instructions forautomatically applying a treatment in portions of the field thatcorrespond with certain CGE values. The term “agricultural prescription”is generally used herein to refer to a set of computer-executableinstructions that is encoded in a software file format suitable forexchange between digital devices (e.g., between the client computingdevice 102 and the remote computing device 106 of FIG. 1 ), and which iscapable of being executed to cause an agricultural implement (e.g., theimplement 104 of FIG. 1 ) and/or attachment (e.g., the attachment 130 ofFIG. 1 ) to perform one or more actions. The agricultural prescriptionmay include complex instructions, such as loops, conditionals, etc. andmay access external APIs (e.g., location information, timing functions,sensor readings, etc.).

For example, the agricultural prescription may access a location module(e.g., a GPS module) of the client computing device 102 to determine thereal-time position of the implement within the field, with respect tothe field map layer associated with CGE values information. Theagricultural prescription may include instructions for causing apre-determined agricultural treatment to be applied to the field, forexample by accessing an attachment (e.g., the attachment 130 of FIG. 1). In this way, the present techniques may be advantageously used toidentify CGE values corresponding a field, which may be highly variablefor the reasons discussed above. The prescription may be able toreactively initiate field management tasks that a human is not capableof performing due to latencies caused by slow reaction time and otherfactors. The present techniques may further advantageously be used toautomatically apply treatment product based on the CGE values, toconserve product while increasing yields.

Example Prescription Map

FIG. 5 is a graph representing an example multi-genetics prescriptionmap 500, according to an embodiment. The map 500 includes a firstproduct coding 502 and a second product coding 504. The first productcoding 502 and the second product coding 504 are depicted withinrespective cells (two of which are designated at reference numerals 506and 508) of a map layer 510. The cells 506, 508 of the map layer 510 maybe color-coded using the first product coding 502 and the second productcoding 504 to indicate cells wherein the respective product will beapplied. For example, the product coding 502 and the product coding 504may be two respective corn varieties determined using the techniquesdescribed herein. The topographic module 152 of FIG. 1 may generate themap layer 510 to include a plurality of predicted CGE values eachcorresponding to a respective field subdivision (e.g., a respectivehexagrid) of the map layer 510. In some embodiments, the map layer 510may be used as a visual tool prior to and/or during the plantingprocess.

Example Corn Growth Efficiency (CGE) Visualizations

In still further embodiments, predicted and/or known CGE values for anagricultural field may be analyzed to generate one or morevisualizations for comparing the potential effects of plantingparticular varieties and/or applying various agricultural products tothe agricultural field. Specifically, in some embodiments, the dataprocessing module 150 of FIG. 1 may include machine-readableinstructions for generating one or more visualizations (e.g., a chart, agraphic, a webpage, etc.) depicting known CGE values and/or predictedCGE values generated by the trained ML models of the CGE determiningmodule 154.

FIG. 6 depicts an example chart 600 for visualizing CGE values at R1among different corn hybrids grown in different locations (e.g.,hexagrids) in a field, according to an embodiment. The chart 600includes a plurality of columns 602 corresponding to respective ones ofa plurality of different sites or locations in one or more agriculturalfields, sub-fields, or portion(s) thereof. The chart 600 includes aplurality of rows 604 corresponding to respective ones of a plurality ofcorn hybrids, and a row 606 containing average CGE values at R1 (e.g.,EFCY_(R1) values determined as described above) for respective ones ofthe locations corresponding to the columns 602. The values shown in therows 604 represent percentages of the corresponding values in the row606. For example, an entry 608 indicates that corn plants for hybrid Agrown in site 610 had a CGE at R1 of 0.85*94.8. Hybrids with higher CGEvalues at R1 may be better suited to a location or environment thanhybrids with lower CGE values, and hybrids with lower CGE values at R1may indicate an opportunity to increase yield through managementpractices that would increase sucrose reserves. For example, hybrids Aand F at R1 are consistently below the site average CGE values and maybenefit from early season management, and hybrid G at R1 is consistentlyabove the site average.

FIG. 7 depicts an example chart 700 for visualizing CGE values at R6among different corn hybrids grown in different locations (e.g.,hexagrids) in a field, according to an embodiment. The chart 700includes a plurality of columns 702 corresponding to respective ones ofa plurality of different sites or locations in one or more agriculturalfields, sub-fields, or portion(s) thereof. The chart 700 includes aplurality of rows 704 corresponding to respective ones of a plurality ofcorn hybrids, and a row 706 containing average CGE values at R6 (e.g.,EFCY_(R6) values determined as described above) for respective ones ofthe locations corresponding to the columns 702. The values shown in therows 704 represent percentages of the corresponding values in the row706. For example, an entry 708 indicates that corn plants for hybrid Agrown in site 710 had a CGE at R6 of 0.6*34.3%. Hybrids with lower CGEvalues at R6 may be better at allocating resources to ear growth, butmay also be at risk for stalk cannibalization, and hybrids with higherCGE values at R6 are less at risk for stalk cannibalization but may notbe as efficient in growing ears of corn. For example, hybrid A holdsonto sucrose at R6 and may need management to extend the grain fillperiod, and hybrid F may need to be watched for stalk integrity.

FIG. 8 depicts an example chart 800 for visualizing classifications oflocations based on CGE values at R1 and R6. The chart 800 includes fourcolumns 802 corresponding to four classes of location represented bycombinations of low CGE at R1, high CGE at R1, low CGE at R6, and highCGE at R6. The chart 800 includes a plurality of rows 804 correspondingto respective ones of a plurality of corn hybrids, and a row 806containing average yield, in bushels per acre, for the respectivelocation classification corresponding to the columns 802. The valuesshown in the rows 804 represent percentages of the corresponding valuesin the row 806. For example, an entry 808 indicates that corn plants forhybrid C grown in a low CGE at R1, and low CGE at R6 environment had ayield of 222.7*106.1%. As shown, hybrid A lacks performance across allsite classifications; hybrid C has a stable yield across low CGE at R1sites; hybrid F favors a high CGE at R1 with increased risk at low CGEat R1 sites; and hybrid G is stable across site classifications. Thegrower or other personnel may review the chart 800 to compare hybridsacross location classifications. Thus, the visualization techniques ofdisclosed techniques advantageously enable growers, field managers, seedcompanies, etc. to quickly compare hybrid performance in a variety ofdifferent environmental conditions.

FIG. 9 depicts an example management recommendation 900 for hybrid Fthat may be determined, for example, based on the example CGE dataand/or the example yield data of FIGS. 6-8 . The recommendation 900includes a plurality of recommendations 904 for respective ones of aplurality of agricultural treatments. For example, recommendations 906and 908 indicate that the late application of fungicide is lessbeneficial than the early application of fungicide, and a recommendation910 indicates that the application of nitrogen in the fall and/orpre-planting in the spring may be very beneficial. The recommendation904 further includes indications 912 that corn plants of hybrid F shouldbe watched in the fall for stalk cannibalization, and may require earlyharvesting.

FIG. 10 depicts an example management recommendation 1000 for hybrid Ethat may be determined, for example, based on the example CGE dataand/or the example yield data of FIGS. 6-8 . The recommendation 1000includes a plurality of recommendations 1004 for respective ones of aplurality of agricultural treatments. For example, recommendations 1006and 1008 indicate, respectively, that the early application of fungicideand the late application of sidedress nitrogen may be very beneficial.The recommendation 1004 further includes indications 1010 that cornplants of hybrid E may require late harvesting, that high amounts offield management may not be needed, and that hybrid E performs wellacross a range of growing environments.

In general, the example data and recommendations of FIGS. 6-10advantageously provide decision-makers in all areas of precisionagriculture with the tools to objectively quantify information relatedto variety performance among different fields and different growingenvironments. The visualization techniques herein enable fast and easyvisualization and communication of objectively quantified information toother parties, thus improving field management computing systems. Forexample, the field advisor need not base product recommendations to afield owner based on intuition, anecdote, or best guess regardingvariety performance.

FIG. 11 is a graph 1100 of a portion of an example agricultural field1102. The graph 1100 indicates a plurality of locations (two of whichare designated at reference numerals 1104 and 1106) within the field1102 at which corn plants were collected at different growth stages. Thecollected corn plants for each location were processed, as describedabove, to determine one or more CGE values for the different growthstages for each location. As also described above, the CGE values wereused to label agronomic data for each location, and the labeledagronomic data was used to train an ML model, such as the example MLmodel 202. As shown, the locations may be selected to correspond to aplurality of different hybrids grown in respective bands (one of whichis designated at reference numeral 1108). As shown, the locations mayalso be selected to represent different growing conditions (e.g., one ormore of soil topography, relative elevation, slope, latitude, growingseason length, incident solar radiation, phosphorus fertility, potassiumfertility, soil wetness index (SWI), organic matter, CEC, planting date,corn hybrid planted, applied agricultural treatments, treatment dates,weather information, environmental stresses, etc.).

Additional Considerations

The above description refers to block diagrams of the accompanyingdrawings. Alternative implementations of the examples represented by theblock diagrams include one or more additional and/or alternativeelements, processes, and/or devices. Additionally and/or alternatively,one or more of the example blocks of the diagrams may be combined,divided, re-arranged, or omitted. Components represented by the blocksof the diagrams may be implemented by hardware, software, firmware,and/or any combination of hardware, software, and/or firmware.

The above description also refers to various operations that are shownin accompanying flowcharts. Any such flowcharts are representative ofexample methods disclosed herein. In some examples, the methodsrepresented by the flowcharts implement the apparatus represented by theblock diagrams. Alternative implementations of example methods disclosedherein may include additional and/or alternative operations. Further,operations of alternative implementations of the methods disclosedherein may combined, divided, re-arranged, or omitted. In some examples,the operations described herein are implemented by machine-readableinstructions (e.g., software and/or firmware) stored on a non-transitoryor tangible machine-readable storage medium for execution by one or moreprocessor. In some examples, the operations described herein areimplemented by one or more configurations of one or more specificallydesigned logic circuits (e.g., ASIC(s), FPGA(s), etc.). In some examplesthe operations described herein are implemented by a combination ofspecifically designed logic circuit(s) and machine-readable instructionsstored on a non-transitory or tangible machine-readable storage mediumfor execution by the processor(s).

As used herein, each of the terms “tangible machine-readable storagemedium” and “non-transitory machine-readable storage medium” isexpressly defined as any type of tangible or non-transitory storagemedium (e.g., a platter of a hard disk drive, a digital versatile disc,a compact disc, flash memory, read-only memory, random-access memory,etc.) on which machine-readable instructions (e.g., program code in theform of, for example, software and/or firmware) can be stored for anysuitable duration of time (e.g., permanently, for an extended period oftime (e.g., while a program associated with the machine-readableinstructions is executing), and/or a short period of time (e.g., whilethe machine-readable instructions are cached and/or during a bufferingprocess)). Further, as used herein, each of the terms “tangiblemachine-readable storage medium” and “non-transitory machine-readablestorage medium” is expressly defined to exclude propagating signals.That is, as used in any claim of this patent, neither of the terms“tangible machine-readable storage medium” and “non-transitorymachine-readable storage medium,” can be read to be implemented by apropagating signal.

The following considerations also apply to the foregoing discussion.Throughout this specification, plural instances may implement operationsor structures described as a single instance. Although individualoperations of one or more methods are illustrated and described asseparate operations, one or more of the individual operations may beperformed concurrently, and nothing requires that the operations beperformed in the order illustrated. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

It should also be understood that, unless a term is expressly defined inthis patent using the sentence “As used herein, the term” “is herebydefined to mean . . . ” or a similar sentence, there is no intent tolimit the meaning of that term, either expressly or by implication,beyond its plain or ordinary meaning, and such term should not beinterpreted to be limited in scope based on any statement made in anysection of this patent (other than the language of the claims). To theextent that any term recited in the claims at the end of this patent isreferred to in this patent in a manner consistent with a single meaning,that is done for sake of clarity only so as to not confuse the reader,and it is not intended that such claim term be limited, by implicationor otherwise, to that single meaning.

Unless a claim element is defined by reciting the word “means” and afunction without the recital of any structure, it is not intended thatthe scope of any claim element be interpreted based on the applicationof 25 U.S.C. § 112(f).

Unless specifically stated otherwise, discussions herein using wordssuch as “processing,” “computing,” “calculating,” “determining,”“presenting,” “displaying,” or the like may refer to actions orprocesses of a machine (e.g., a computer) that manipulates or transformsdata represented as physical (e.g., electronic, magnetic, or optical)quantities within one or more memories (e.g., volatile memory,non-volatile memory, or a combination thereof), registers, or othermachine components that receive, store, transmit, or displayinformation.

As used herein any reference to “one embodiment,” “an embodiment,” “oneexample,” or “an example” means that a particular element, feature,structure, or characteristic described in connection with the embodimentor example is included in at least one embodiment or example. Theappearances of the phrase “in one embodiment” or “in one example” invarious places in the specification are not necessarily all referring tothe same embodiment or example.

As used herein, the terms “comprises,” “comprising,” “includes,”“including,” “has,” “having” or any other variation thereof, areintended to cover a non-exclusive inclusion. For example, a process,method, article, or apparatus that comprises a list of elements is notnecessarily limited to only those elements but may include otherelements not expressly listed or inherent to such process, method,article, or apparatus. Further, unless expressly stated to the contrary,“or” refers to an inclusive or and not to an exclusive or. For example,a condition A or B is satisfied by any one of the following: A is true(or present) and B is false (or not present), A is false (or notpresent) and B is true (or present), and both A and B are true (orpresent). As used herein, the phrase “at least one of A and B” isintended to refer to any combination or subset of A and B such as (1) atleast one A, (2) at least one B, and (3) at least one A and at least oneB. Similarly, the phrase “at least one of A or B” is intended to referto any combination or subset of A and B such as (1) at least one A, (2)at least one B, and (3) at least one A and at least one B.

In addition, use of “a” or “an” is employed to describe elements andcomponents of the embodiments herein. This is done merely forconvenience and to give a general sense of the invention. Thisdescription should be read to include one or at least one and thesingular also includes the plural unless it is obvious that it is meantotherwise.

Further, as used herein, the expressions “in communication,” “coupled”and “connected,” including variations thereof, encompasses directcommunication and/or indirect communication through one or moreintermediary components, and does not require direct mechanical orphysical (e.g., wired) communication and/or constant communication, butrather additionally includes selective communication at periodicintervals, scheduled intervals, aperiodic intervals, and/or one-timeevents. The embodiments are not limited in this context.

Upon reading this disclosure, those of ordinary skill in the art willappreciate still additional alternative structural and functionaldesigns for implementing the concepts disclosed herein, through theprinciples disclosed herein. Thus, while particular embodiments andapplications have been illustrated and described, it is to be understoodthat the disclosed embodiments are not limited to the preciseconstruction and components disclosed herein. Various modifications,changes and variations, which will be apparent to those of ordinaryskill in the art, may be made in the arrangement, operation, and detailsof the method and apparatus disclosed herein without departing from thespirit and scope defined in the appended claims.

What is claimed:
 1. A computing system, comprising: one or moreprocessors; and one or more non-transitory, computer-readable storagemedia storing: a machine learning (ML) model trained using a trainingagronomic data set corresponding to one or more trial agriculturalfields, the training agronomic data set labeled with one or more knowncorn growth efficiency (CGE) values; and machine-readable instructionsthat, when executed by the one or more processors, cause the computingsystem to: process one or more input feature vectors corresponding to anagronomic data set with the ML model to generate one or more predictedCGE values for one or more portions of an agricultural field; andprovide the one or more predicted CGE values as an output.
 2. Thecomputing system of claim 1, wherein the instructions, when executed bythe one or more processors, cause the computing system to: determine anagricultural prescription for the agricultural field based on thepredicted CGE values.
 3. The computing system of claim 2, wherein theagricultural prescription includes at least one of: (i) one or more cornhybrids to be planted in the one or more portions of the agriculturalfield; or (ii) target dates for planting the one or more portions of theagricultural field.
 4. The computing system of claim 2, wherein theagricultural prescription includes at least one of: (i) one or moreagricultural treatments to be applied to the one or more portions of theagricultural field; or (ii) target dates for applying the one or moreagricultural treatments.
 5. The computing system of claim 2, wherein theinstructions, when executed by the one or more processors, cause thecomputing system to: provide the agricultural prescription to a secondcomputing system for execution by the second computing system to applythe agricultural prescription to the agricultural field.
 6. Thecomputing system of claim 1, wherein a first predicted CGE value is fora first plant growth stage, and wherein a second predicted CGE value isfor a second plant growth stage.
 7. The computing system of claim 6,wherein the first plant growth stage is an R1 growth stage, and thesecond plant growth stage is a R6 growth stage.
 8. The computing systemof claim 1, wherein a CGE value is based on an amount of stored sucrose.9. The computing system of claim 8, wherein the CGE value is also basedon an amount of dry plant matter.
 10. The computing system of claim 1,wherein a CGE value is based on remotely-sensed data.
 11. The computingsystem of claim 10, wherein the remotely-sensed data represents one ormore multi-spectral images taken of the one or more trial agriculturalfields.
 12. A computing system, comprising: one or more processors; andone or more non-transitory, computer-readable storage media storingmachine-readable instructions that, when executed by the one or moreprocessors, cause the computing system to: process an agronomic data setwith a machine learning (ML) model to generate one or more predictedcorn growth efficiency (CGE) values, the agronomic data set beinglabeled with one or more known CGE values; and modify one or moreparameters of the ML model.
 13. The computing system of claim 12,wherein the instructions, when executed by the one or more processors,cause the computing system to: generate a hybrid management ratingprofile; and present the hybrid management rating profile as an output.14. The computing system of claim 12, wherein at least one of the one ormore known CGE values is based on a measured sucrose reserve.
 15. Thecomputing system of claim 14, wherein the at least one CGE value isbased on first corn plants collected from one or more agriculturalfields at a first growth stage, and wherein the sucrose reserverepresents an amount of sucrose stored in one or more internodes of thefirst corn plants.
 16. The computing system of claim 15, wherein theknown CGE value is also based on a measured amount of dry plant matter.17. The computing system of claim 16, wherein the measured amount of dryplant matter represents a ratio of: (i) a first amount of dry plantmatter measured in the first corn plants; and (ii) a difference betweenthe first amount of dry plant matter and a second amount of dry plantmatter measured in second corn plants collected from the one or moreagricultural fields at a second growth stage.
 18. The computing systemof claim 17, wherein the first growth stage is an R1 growth stage, andthe second growth stage is a V6 growth stage.
 19. The computing systemof claim 12, wherein the instructions, when executed by the one or moreprocessors, cause the computing system to: access one or moremulti-spectral images taken of one or more agricultural fields; andanalyze the one or more images to determine a known CGE value.
 20. Thecomputing system of claim 12, wherein the agronomic data set includesmachine data representing one or more measurements taken using a soilprobe.
 21. The computing system of claim 12, wherein the agronomic dataset includes topographic data.
 22. The computing system of claim 12,wherein the agronomic data set includes at least one of: (i) one or moreagricultural treatments applied to one or more agricultural fields; or(ii) dates when the agricultural treatments were applied.
 23. Thecomputing system of claim 12, wherein the agronomic data set includes atleast one of: (i) one or more corn hybrids planted in one or moreportions of one or more agricultural fields; or (ii) dates when the oneor more portions were planted.
 24. The computing system of claim 12,wherein the agronomic data set includes weather information for one ormore agricultural fields.
 25. A computer-implemented method, comprising:processing, using one or more processors, an agronomic data set with amachine learning (ML) model to generate one or more predicted corngrowth efficiency (CGE) values, the agronomic data set being labeledwith one or more known CGE values; and modifying, using one or moreprocessors, one or more parameters of the ML model.
 26. The method ofclaim 25, wherein at least one of the known CGE values is based on ameasured sucrose reserve.
 27. The method of claim 26, wherein the atleast one of the known CGE values is based on first corn plantscollected from one or more trial agricultural fields at a first growthstage, and wherein the sucrose reserve represents an amount of sucrosestored in one or more internodes of the first corn plants.
 28. Themethod of claim 27, wherein the at least one of the known CGE values isalso based on a measured amount of dry plant matter.
 29. The method ofclaim 28, wherein the measured amount of dry plant matter represents aratio of (i) a first amount of dry plant matter measured in the firstcorn plants; and (ii) a difference between the first amount of dry plantmatter and a second amount of dry plant matter measured in second cornplants collected from the one or more trial agricultural fields at asecond growth stage.
 30. The method of claim 29, wherein the firstgrowth stage is an R1 growth stage, and the second growth stage is a V6growth stage.